Solid tumors are a heterogeneous collection of cells with vastly different capacities to proliferate, metastasize, and resist therapy. Although functionl diversity among individual tumor cells is widely recognized, we do not know how many functional states there truly are, nor is it clear how best to catalog those states in the first plce. The global profile of mRNAs expressed by a cell can suggest its state, provided that the measurements are reliable and can be obtained with minimal disruption of the cell in its native context. To date, neither of these criteria has been achieved for solid tumors. We circumvented the problem by developing a new method, called stochastic profiling, which measures small 10-cell pools of cells microdissected in situ to glean single- cell information through statistical analysis. The 10-cell pools increase the starting material and allow reliable expression profiles to be achieved with samples microdissected in situ. Previously, we have used stochastic profiling to uncover a wealth of single-cell functional states in 3D organotypic cultures of breast epithelial cells. In our answer to PQB4, we seek to address whether stochastic profiling can be directly applied to human or murine solid tumors and yield meaningful information about cancer progression. The hypothesis is that progression is linked to a common subset of regulatory states that change in frequency or identity as solid tumors become more advanced. The aims of this proposal are: 1) To evaluate ex vivo regulatory heterogeneities within genetically engineered small-cell lung cancers at various stages of progression. We will use stochastic profiling with premalignant cells and small-cell lung tumorspheres from mice with Trp53 and Rb conditionally deleted in neuroendocrine cells. 2) To evaluate in vivo regulatory heterogeneities within genetically engineered gliomas at various stages of progression. We will use fluorescence-guided stochastic profiling to evaluate gliomagenesis in mice with Trp53 and Nf1 conditionally deleted in oligodendrocyte precursor cells of the olfactory bulb. 3) To test whether regulatory heterogeneities in human tumors are quantitatively predictive of pathologic stage and grade. We will combine stochastic profiling of breast tumors with partial least squares regression to link single-cell regulatory states to clinical parameters. If successful, this application would set the stage for a long-term goal of identifying all major categories of regulatory heterogeneity in solid tumors. To characterize the functional state of individual tumor cells in context, the answer may be to avoid measuring single cells entirely.